问TensorFlow中保存和恢复网络的不同推理结果

EN

import tensorflow as tf
import network as net

...

checkpoint_prefix = os.path.join(path_to_save, "saved_checkpoint")
checkpoint_state_name = "checkpoint_state"
input_graph_name = "input_graph.pb"
output_graph_name = "output_graph.pb"

# # FOR TRAINING IMAGES
image, label = _get_image_and_label(learning_params)
image_batch, label_batch = tf.train.shuffle_batch([image, label], batch_size=mini_batch_size, num_threads=8,
                                                  capacity=min_queue_examples_train + 3*mini_batch_size,
                                                  min_after_dequeue=min_queue_examples_train)

label_batch = tf.reshape(label_batch, [mini_batch_size, label_dim])


# # FOR TEST IMAGES
image_, label_ = _get_image_and_label_eval(learning_params)
image_batch_, label_batch_ = tf.train.batch([image_, label_], batch_size=mini_batch_size, num_threads=1,
                                            capacity=min_queue_examples_test)

label_batch_ = tf.reshape(label_batch_, [mini_batch_size, label_dim])


image_batch = tf.placeholder_with_default(image_batch, shape=
[mini_batch_size, height, width, depth])
label_batch = tf.placeholder_with_default(label_batch, shape=
[mini_batch_size, label_dim])

image_batch_ = tf.placeholder_with_default(image_batch_, shape=
[mini_batch_size, height, width, depth])
label_batch_ = tf.placeholder_with_default(label_batch_, shape=
[mini_batch_size, label_dim])

# construct graph for CNN
cost, train_op, regression = net.CNN()

saver = tf.train.Saver(max_to_keep=1)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    ....

    tf.train.write_graph(sess.graph_def, path_to_save, input_graph_name, as_text=False)

    for epoch in range(num_epochs):

        # # TRAINING
        for step in range(num_batches_train):

            X, Y = sess.run([image_batch, label_batch])
            cost_value, _ = sess.run([cost, train_op], feed_dict={net.X: X, net.Y: Y})
            cost_buffer.append(cost_value)


        # # EVALUATION
        for step in range(num_batches_test):
            X, Y = sess.run([image_batch_, label_batch_])
            inference = sess.run(regression, feed_dict={net.X: X})
            error = abs(inference - Y)

        # # SAVE TRAINED NETWORK WHEN THE BEST PERFORMER APPEARS
        if min_Cost > np.mean(cost_buffer):
            min_Cost = np.mean(cost_buffer)
            checkpoint_path = saver.save(sess, checkpoint_prefix, global_step=0, latest_filename=checkpoint_state_name)

    coord.request_stop()
    coord.join(threads)

sess.close()

import tensorflow as tf

...

image, label = _get_image_and_label_eval()
image_batch, label_batch = tf.train.batch([image, label], batch_size=mini_batch_size, num_threads=1, capacity=min_queue_examples)

label_batch = tf.reshape(label_batch, [mini_batch_size, 3])


with tf.Session() as sess:

    # Step1) Initialize global variables
    sess.run(tf.global_variables_initializer())

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    # Step2) Load graph from meta file (meta file contains the graph that I had defined before)
    saver = tf.train.import_meta_graph(os.path.join(path_to_saved_network, 'saved_checkpoint-0.meta'))

    # Step3) Restore all the weights values
    saver.restore(sess, os.path.join(net_dir, 'saved_checkpoint-0'))
    print('Trained Deep Network is restored')

    # Step4) Recall placeholder and operation
    graph = tf.get_default_graph()
    x = graph.get_tensor_by_name("input_node:0")
    r = graph.get_tensor_by_name("output_node:0")

    # # EVALUATION
    for step in range(num_batches):
        X, Y = sess.run([image_batch, label_batch])
        inference = sess.run(r, feed_dict={x: X})
        error = abs(inference - Y)

    coord.request_stop()
    coord.join(threads)

sess.close()